As an essential function of an autonomous vehicle, an automatic decision-making function can select corresponding driving strategies based on perceived states of surrounding impediments and corresponding road structures, to ensure driving safety and comfort. During the real driving, there exist diverse road structures and constantly changing traffic scenes. How to make a correct decision in various complex scenes to guarantee the driving safety and comfort is a problem that presently needs to be solved.
Currently, there are two automatic decision-making schemes: one is an expert system scheme, namely automatic decision-making is implemented by extracting corresponding autonomous driving rules based on the expertise of experienced drivers, and triggering the corresponding rules using traffic scenes and road structures where vehicles are. The other one is a machine learning scheme, that is, a mapping relation between traffic scenes and road structures and drivers' decisions is learned using a machine learning model based on driving data collected from experienced drivers.
However, the rules extracted according to the expert system scheme are difficult to encompass all traffic scenes. Particularly, when more and more traffic scenes need to be covered, how to guarantee non-conflicting quadrature of the rules is an arduous task. Whereas in the machine learning scheme, it is required that data must cover enough traffic scenes and road structures. However, when encountering scenes and roads never learned before, this scheme is difficult to guarantee correctness of a decision strategy and the driving safety. Therefore, the existing automatic decision-making schemes are merely applied to simple traffic scenes and are difficult to be used in complex road structures and constantly changing traffic scenes.